Pliable rejection sampling

Erraqabi, Akram; Valko, Michal; Carpentier, Alexandra; Maillard, Odalric-Ambrym

Document type :

Communication dans un congrès avec actes

Title :

Pliable rejection sampling

Author(s) :

Erraqabi, Akram [Auteur]
Sequential Learning [SEQUEL]
Valko, Michal [Auteur]

Sequential Learning [SEQUEL]
Carpentier, Alexandra [Auteur]
Institut für Mathematik [Potsdam]
Maillard, Odalric-Ambrym [Auteur]
Machine Learning and Optimisation [TAO]

Conference title :

International Conference on Machine Learning

City :

New York City

Country :

Etats-Unis d'Amérique

Start date of the conference :

2016-06-19

HAL domain(s) :

Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

Rejection sampling is a technique for sampling from difficult distributions. However, its use is limited due to a high rejection rate. Common adaptive rejection sampling methods either work only for very specific distributions ...
Show more >Rejection sampling is a technique for sampling from difficult distributions. However, its use is limited due to a high rejection rate. Common adaptive rejection sampling methods either work only for very specific distributions or without performance guarantees. In this paper, we present pliable rejection sampling (PRS), a new approach to rejection sampling, where we learn the sampling proposal using a kernel estimator. Since our method builds on rejection sampling, the samples obtained are with high probability i.i.d. and distributed according to f. Moreover, PRS comes with a guarantee on the number of accepted samples.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Extraction et transfert de connaissances dans l'apprentissage par renforcement

Collections :